Datasets: We evaluate our method on the DomainNet and DomainNet-LS benchmarks for the task of ZSLDG, as in [1]}. DomainNet [2]}:
It is a large-scale dataset and is currently the only benchmark dataset for the ZSLDG setting [1]}. It consists of nearly \(0.6\)  million images from 345 categories in 6 domains: painting, clipart, sketch, infograph, quickdraw and real. For the task of ZSLDG, we follow the same training/validation/testing splits along with the training and evaluation protocol described in [1]}. In particular, 45 out of 345 are fixed as unseen classes and training is performed using only the remaining seen class images. Among the 6 domains in DomainNet, the seen class images from 5 domains are provided during training, and the model is evaluated on the 45 unseen classes in the held-out (unseen) domain. We repeat experiments with each of the domains as the unseen domain. Following [1]}, the real domain is never held out since a ResNet-50 backbone, pre-trained on ImageNet [6]}, is employed. Average per-class accuracy is used as the performance metric for evaluation on the held-out domain. Similarly, we use the word2vec [7]} representations as the semantic information for inter-relating seen and unseen classes, as in [1]}.
